AI-Genetic-Pro

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LICENSE  view on Meta::CPAN

AND MODIFICATION

0. This License Agreement applies to any software library or
other program which contains a notice placed by the
copyright holder or other authorized party saying it may be
distributed under the terms of this Lesser General Public
License (also called "this License"). Each licensee is
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application programs (which use some of those functions
and data) to form executables.

The "Library", below, refers to any such software library or
work which has been distributed under these terms. A "work
based on the Library" means either the Library or any
derivative work under copyright law: that is to say, a work
containing the Library or a portion of it, either verbatim or with
modifications and/or translated straightforwardly into another
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limitation in the term "modification".)

t/01_inject.t  view on Meta::CPAN

);

# init population of 32-bit vectors
$ga->init(BITS);

my $population = [ ];
for my $chromosome(@{$ga->chromosomes}){
	push @$population, $chromosome->clone;
}

my @data;
for(0..BITS){
	my @chromosome;
	push @chromosome, rand() < 0.5 ? 1 : 0 for 1..BITS;
	push @data, \@chromosome;
}

push @$population, @data;
$ga->inject(\@data);

my $OK = 1;
for(0..$#$population){
	my @tmp0 = @{$population->[$_]};
	my @tmp1 = @{$ga->chromosomes->[$_]};
	unless(compare(\@tmp0, \@tmp1)){
		$OK = 0;
		last;
	}
}

t/06_listvectors_constant_length.t  view on Meta::CPAN

        -parents         => 2,                # number  of parents
        -selection       => [ 'Roulette' ],   # selection strategy
        -strategy        => [ 'Points', 2 ],  # crossover strategy
        -cache           => 1,                # cache results
        -history         => 0,                # remember best results
        -preserve        => 0,                # remember the bests
        -variable_length => 0,                # turn variable length OFF
);


my @data;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);

@data = (
	[qw( 4 0 4 0 4 0 4 0 )],
	[qw( 0 4 0 4 0 4 0 4 )],
	[qw( 4 4 0 0 4 4 0 0 )],
	[qw( 4 4 4 4 0 0 0 0 )],
	[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);

# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));

t/07_listvectors_variable_length_I.t  view on Meta::CPAN

        -parents         => 2,                # number  of parents
        -selection       => [ 'Roulette' ],   # selection strategy
        -strategy        => [ 'Points', 2 ],  # crossover strategy
        -cache           => 1,                # cache results
        -history         => 0,                # remember best results
        -preserve        => 0,                # remember the bests
        -variable_length => 1,                # turn variable length OFF
);


my @data;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);

@data = (
	[qw( 4 0 4 0 4 0 4 0 )],
	[qw( 0 4 0 4 0 4 0 4 )],
	[qw( 4 4 0 0 4 4 0 0 )],
	[qw( 4 4 4 4 0 0 0 0 )],
	[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);

# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));

t/08_listvectors_variable_length_II.t  view on Meta::CPAN

        -parents         => 2,                # number  of parents
        -selection       => [ 'Roulette' ],   # selection strategy
        -strategy        => [ 'Points', 2 ],  # crossover strategy
        -cache           => 1,                # cache results
        -history         => 0,                # remember best results
        -preserve        => 0,                # remember the bests
        -variable_length => 2,                # turn variable length OFF
);


my @data;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);

@data = (
	[qw( 4 0 4 0 4 0 4 0 )],
	[qw( 0 4 0 4 0 4 0 4 )],
	[qw( 4 4 0 0 4 4 0 0 )],
	[qw( 4 4 4 4 0 0 0 0 )],
	[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);

# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));

t/09_rangevectors_constant_length.t  view on Meta::CPAN

        -parents         => 2,                # number  of parents
        -selection       => [ 'Roulette' ],   # selection strategy
        -strategy        => [ 'Points', 2 ],  # crossover strategy
        -cache           => 1,                # cache results
        -history         => 0,                # remember best results
        -preserve        => 0,                # remember the bests
        -variable_length => 0,                # turn variable length OFF
);


my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);

@data = (
	[qw( 4 0 4 0 4 0 4 0 )],
	[qw( 0 4 0 4 0 4 0 4 )],
	[qw( 4 4 0 0 4 4 0 0 )],
	[qw( 4 4 4 4 0 0 0 0 )],
	[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);

# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));

t/10_rangevectors_variable_length_I.t  view on Meta::CPAN

        -parents         => 2,                # number  of parents
        -selection       => [ 'Roulette' ],   # selection strategy
        -strategy        => [ 'Points', 2 ],  # crossover strategy
        -cache           => 1,                # cache results
        -history         => 0,                # remember best results
        -preserve        => 0,                # remember the bests
        -variable_length => 1,                # turn variable length OFF
);


my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);

@data = (
	[qw( 4 0 4 0 4 0 4 0 )],
	[qw( 0 4 0 4 0 4 0 4 )],
	[qw( 4 4 0 0 4 4 0 0 )],
	[qw( 4 4 4 4 0 0 0 0 )],
	[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);

# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));

t/11_rangevectors_variable_length_II.t  view on Meta::CPAN

        -parents         => 2,                # number  of parents
        -selection       => [ 'Roulette' ],   # selection strategy
        -strategy        => [ 'Points', 2 ],  # crossover strategy
        -cache           => 1,                # cache results
        -history         => 0,                # remember best results
        -preserve        => 0,                # remember the bests
        -variable_length => 2,                # turn variable length OFF
);


my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);

@data = (
	[qw( 4 0 4 0 4 0 4 0 )],
	[qw( 0 4 0 4 0 4 0 4 )],
	[qw( 4 4 0 0 4 4 0 0 )],
	[qw( 4 4 4 4 0 0 0 0 )],
	[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);

# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));

t/12_combinations_constant_length.t  view on Meta::CPAN

        -strategy        => [ 'PMX' ],        # crossover strategy
        -cache           => 1,                # cache results
        -history         => 0,                # remember best results
        -preserve        => 0,                # remember the bests
        -variable_length => 0,                # turn variable length OFF
);


$ga->init( [ 'a'..'h' ] );

my @data = (
	[qw( a c b d e g f h )],
	[qw( a b d c e f h g )],
	[qw( a c b d f e g h )],
	[qw( h b c d e f g a )],
);

push @data, @data for 1..scalar(@Win);
$ga->inject(\@data);

# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));

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